The Effects of High School Athletic Participation and Extra Curricular Activities on Earnings

1. The Effects of High School Athletic Participation and Extra Curricular Activities on Earnings Presented by: Douglas Brewer The University of Akron; spring 2006

2. Introduction • The Economy in the United States is facing many new challenges that require creative solutions to foster long term growth and stability for the American worker. • One of the main issues facing the Labor Market today is outsourcing. This 21st century phenomena has left hundred of thousands of people structurally unemployed without the necessary tools to make the transition into another field. • One potential solution to this problem is to increase the levels of education and training for our citizens so that these challenges can be met and overcome with steadfast resolution.

3. Literature Review • Data from the National Longitudinal Survey’s examined and tested in sociological papers indicates that men avg. age 32 who participated high school athletics earn 31% higher wages (Survey of Youth) and men avg. 31 had 12% higher wages (High School Class of 1972)….are these findings accurate? • Baron et al. (2000) (Economic paper) implemented controls and utilized a simple allocation of time model to more accurately describe effects of athletic participation on labor market outcomes—findings indicate that high school athletes are more likely to achieve greater earnings but not at levels that sociological papers indicated. (men only)

4. Literature Review Continued • Ewing (1997) discovered that former high school athletic participants were more likely to be in jobs that had better labor market outcomes as compared to their non-athletic counterparts. • Long et al. (1991) explores the earnings based on athletic participation on the collegiate level. Long determined that men associated with college level athletic programs earned 4 % higher incomes compared to those men that did not have this particular affiliation. • This study also looked at the earning levels for women in this same context and found no advantages for them. • Male varsity athletes received greater incomes 10 years after entering college and were more likely to graduate.

5. Literature Review Continued • Picou, McCarter, and Howell (1985) investigated high school athletic participation 11 years after graduation for white and blacks by gender.

6. Hypothesis/Research Question • This paper will measure the effects that high school athletics, pep, and cheer clubs have in relation to labor income. If individuals participated in high school athletic, pep or cheer clubs then they should receive higher labor incomes as this is an additional form of training added to their educational level.

7. Motivation/Goal of Study • Sociology papers ignore human capital model of income determination. • Sociological papers also ignore variables known to affects wages, which may bias results. • Barron et al. (2000) showed results utilizing data from 1972. • Testing my hypothesis with the Mincer equation, I will update the existing Knowledge from the NLSY 1972 with data from 2000.

8. Theory • The principal theory that is the foundation of this project is Becker’s Human Capital Theory. Essentially, those individuals that expect greater future benefits from additional human capital investments, like education and training, will acquire more human capital. • This applies directly to this study in that those individuals that invest in extra curricular activities should ultimately receive greater labor incomes.

9. Empirical Setup (Methodology) • The model that I use in the project is based on the Mincer equation. More specifically: • logLabor Income= β1Athletics + β2Education + β3AFQT + β4Experience + β5ExperienceSQR + β6Age + β7Race + µi • Labor Income is total income derived from wages and salary (2000). • Athletics equals the participation in athletic, pep, and cheer clubs (1984). • Education is equal to the respondent’s highest degree ever received as of 2000. • Race is the variable which describes the respondent’s status (white, black, or other {each of these represent a dummy variable}).

10. Empirical Setup (Methodology) Continued • AFQT is the variable that explains the respondent’s cognitive ability. The AFQT is the Armed Forces Qualification Test. • Experience is a dummy variable that is proxy for a person’s labor market experience. (Age-Education-6) • ExperienceSQR is tabulated by multiplying Experience by Experience. (Requirement of the Mincer equation) • Age is the variable which represents respondents age as of 2000.

11. Preliminary Data/Results

12. Preliminary Data/Results Continued

13. Preliminary Data/Results Continued • The Adjusted R-squared value of 0.1517 indicates that 15 % of the variation in the movement of the dependent variable is explained.

14. Conclusions • The foundation of a productive economy is the productivity levels of the individual workers. • Increasing Education and Training levels has been shown to enable people to harness their competitive abilities and achieve greater labor incomes. • Cutting extra curricular activities and educational programs will create long term problems for the next generation of workers.